Model-Free Online Recursive Optimization Method for Batch Process Based on Variable Period Decomposition

ABSTRACT

The present invention discloses a model-free online recursive optimization method for a batch process based on variable period decomposition. Variable operation data closely related to product quality is acquired, optimization action on each subset is integrated on the basis of time domain variable division on the process by utilizing a data driving method and a global optimization strategy is formed, based on which an online recursive error correction optimization strategy is implemented. According to the method, the online optimization strategy is formed completely based on the operation data of the batch process without needing prior knowledge or a model of a process mechanism. Meanwhile, the optimized operation locus line has better adaptability by using the online recursive correction strategy, and thus the anti-interference requirement of the actual industrial production is better met.

TECHNICAL FIELD

The present invention belongs to the field of chemical processmanufacturing industry, and relates to an operation locus line variableperiod decomposition model-free online recursive optimization method fora batch process, which is applicable to optimal operation locus lineonline optimization of batch reactors, batch rectifying towers, batchdrying, batch fermentation, batch crystallization and other processesand systems operated by adopting batch modes.

BACKGROUND

A batch process refers to that in industrial practical production,operators usually start from a variety of control indicators to find outan operation curve for a specific batch process based on long-timeaccumulated experience. Such a method of seeking an operation curve bymeans of experience is laborious, time-consuming and difficult tostandardize and popularize. Thus, it is necessary to carry out a simpleand effective batch process optimization method to obtain moresatisfying economic indicators. The optimization of the batch process isusually aimed at improving the quality or yield of a product to obtainan optimal operation locus line. Therefore, the study on the method ofobtaining the optimal operation curve of the batch process is the key tosolve the problem.

The most common method of obtaining the optimal operation curve is amodel-based offline optimization method, which is to offline solve theoptimization problem based on a process model. However, the offlineoptimization is only applicable to an ideal model, and whenuncertainties and disturbances in the process model affect the real-timeoperation of a system, the obtained locus line will no longer beoptimal. At the same time, operation strategies and operation conditionsof the process are required to be updated in real time by the change ofa feedstock during operation, switching of products and raw materialsand start/stop of the production process. Therefore, it is an importanttopic in the process industry to study the online real-time optimizationmethod and technology for the batch process.

There have been many successful industrial application cases onreal-time optimization based on a continuous process, whereas for theonline real-time optimization technology for a batch process, there isstill a lack of relatively general effective method suitable forindustrial application. Therefore, it is extremely urgent to put forwarda batch process online real-time optimization strategy and animplementation framework, which are relatively general and can solvereal industrial problems to promote the industrialized process of onlinereal-time optimization of the batch process, so as to provide a newmethod for solving the control problem in the field of practicalproduction.

SUMMARY

The present invention relates to a variable period decompositionmodel-free online recursive optimization method for a batch process.Variable operation data closely related to product quality is acquired,optimization action on each subset is integrated on the basis of timedomain variable division on the process by utilizing a data drivingmethod and a global optimization strategy is formed, based on whichonline recursive minimum error correction of the optimization strategyis implemented.

In order to fulfill the above purpose, the present invention adopts thefollowing technical solution.

A data driven online recursive optimization method for variable perioddecomposition of a batch process is completely based on operation dataof a production process, and does not need prior knowledge or amechanism model of a process mechanism

The s of the present invention are divided into two parts. The firstpart refers to offline data acquisition and establishment of a basicoptimization strategy; and the second part refers to an online recursiveerror correction implementation method.

The offline data acquisition and basic optimization strategy step is asfollows.

Step 1: For operating a complete batch process, variables to beoptimized and final quality or yield indicators are acquired in batches.The acquisition time intervals of data may be equal time intervals orunequal time intervals, and within one time interval, theto-be-optimized variable of the process does not have a significantchange or have a significant impact on the final quality or yieldindicator. Generally, 30-50 batches of effective data are required.

Step 2: For the acquired data, principal component analysis is performedon the variables in batches, and singular points are removed from aprincipal component mode diagram, so as to enable all data points to bewithin one degree of credibility.

Step 3: Equal interval division or unequal interval division isperformed on the remaining data after the singular points are removed ona time axis.

Step 4: Each batch of data included in each interval is expressed as acontinuous variable, and these variables are referred to as decomposedperiod variables. The value of the period variable is composed of eachbatch of data of the variable to be optimized within a specific timeinterval.

Step 5: Each corresponding batch quality or yield indicator in step 4 isreferred to as an indicator variable. A value of the indicator variableis a continuous variable formed by the final quality or yield of eachbatch.

Step 6: The period variables and the indicator variables formed in step4 and step 5 are combined to form a combined data matrix of the periodvariables and the indicator variables.

Step 7: Principal component analysis is performed on the above-mentionedcombined matrix to form a principal component load diagram.

Step 8: The action directions and magnitudes of the period variables onthe indicator variables are classified for the principal component loaddiagram in step 7. They are classified into positive action, reverseaction and no (micro) action.

Step 9: An optimization strategy for each period variable is calculatedaccording to the following perturbation formula:

J(i)=M(i)+sign(i)×3σ(i)

wherein J(i), M(i) and σ(i) herein are respectively optimization targetvalue, mean value and standard deviation of the ith period variable; andsign(i) is a cosine symbol of an included angle formed by the ith periodvariable and the indicator variable. The sign(i) is +1 when the includedangle is smaller than 90 degrees, −1 when the included angle is greaterthan 90 degrees, and 0 when the included angle is equal to 90 degrees.

Step 10: The optimization target values of all periods obtained in step9 constitute a basic optimization variable curve for the whole batchprocess according to a period sequence i=1, 2, . . . , N.

Step 11: The optimization variable curve is usually digitally filtered,so that the new optimization curve is relatively smooth and facilitatestracking control.

In order to overcome dynamic control deviation and uncontrollable randomdisturbance, when the basic optimization control variable locus obtainedby the above steps is put into practical application, online recursiveerror correction is performed on the basic optimization strategy at eachtime period.

The online recursive error correction steps are as follows.

Step 12: In the (i−1)th time period, the error of the offline basicoptimization target value J(i−1) and the actual measured value RV(i−1)is calculated:

E(i−1)=J(i−1)−RV(i −1)

Step 13: On the offline basic optimization strategy, a new optimizationtarget value of next period is constituted:

J _(o)(i)=J(i)+E(i−1)

Step 12 and step 13 are sequentially calculated according to the periodsequence i=1, 2, . . . , N and applied to the process, till theoperation of the whole batch process is over.

More generally, in step 12, an error sequence can be formed by using theerrors of a plurality of past periods, the error sequence is digitallyfiltered, and the filtered prediction value is applied to theoptimization strategy of the current period.

According to the present invention, variable operation data closelyrelated to product quality is acquired, optimization action on eachsubset is integrated on the basis of time domain variable division onthe process by utilizing a data driving method and a global optimizationstrategy is formed, based on which online recursive minimum errorcorrection of an optimization strategy is implemented. According to themethod of the present invention, the online optimization strategy isformed completely based on the operation data of the batch processwithout needing prior knowledge or a model of a process mechanism.Meanwhile, the optimized operation locus line has better adaptability byusing the online recursive correction strategy, and thus theanti-interference requirement of the actual industrial production isbetter met.

BRIEF DESCRIPTION OF FIGURES

FIG. 1 is a temperature curve example of a batch process.

FIG. 2 is a principal component mode diagram indicating that thetemperature of a batch process is an optimization variable.

FIG. 3 is a composition diagram of period variables.

FIG. 4 is a principal component load diagram of period variables andindicator variables.

FIG. 5 is an action classification diagram of the period variables onthe indicator variables.

FIG. 6 is a comparison diagram of an optimized temperature curve and anoriginal temperature curve of a batch process.

FIG. 7 is a generation diagram of an online recursive error correctionstrategy.

FIG. 8 is a block diagram of implementation steps of the presentinvention.

FIG. 9 shows an optimized curve after moving average filtering and anoriginal optimized curve.

FIG. 10 is an optimization result (partial) diagram of a batchcrystallization process.

DETAILED DESCRIPTION

A batch crystallization process is taken as the example, and the methoddoes not limit the scope of the present invention.

This implementation method is divided into four parts. The first part isdata acquisition and preprocessing. The second part is construction of acombined data matrix. The third part is calculation of a basicoptimization strategy. The fourth part is establishment of a recursiveerror correction online optimization strategy.

The block diagram of the implementation steps of the present method isshown as FIG. 8, and the specific implementation steps and algorithmsare as follows.

Step 1: For operating a complete batch crystallization process,operation temperature closely related to product yield is selected as avariable to be optimized, and 50 groups of temperature variables andfinal yield indicator data are acquired in batches. The acquisition timeinterval of the data is 1 minute. FIG. 1 is a temperature curve dataacquisition example of a batch crystallization process, and for the sakeof clarity, only the temperature curves of 2 batches are drawn in thefigure.

Step 2: For all the acquired 50 batches of temperature data, principalcomponent analysis is performed on the temperature variables in batches,and singular points are removed from a principal component mode diagram,so that all data points are within one degree of credibility. FIG. 2 isa principal component mode diagram indicating that the temperature of abatch process is an optimization variable, and it can be seen from thefigure that a batch of temperature data on the right is greatlydifferent from the overall data mode and thus should be removed.

Step 3: The remaining 49 batches of temperature data are divided into300 periods at equal intervals on a time axis to constitute 300 periodvariables C1, C2, . . . , C300. For the sake of clarity, FIG. 3 showsC40 to C70 period variables.

Step 4: Each corresponding batch of yield indicator data in step 3 formsan indicator variable Q.

Step 5: The 300 period variables C1, C2, . . . , C300 and one indicatorvariable Q formed in step 3 and step 4 are combined to generate a49×301-dimensional combined data matrix L.

Step 6: Principal component analysis is performed on the combined matrixL to form a principal component load diagram. For the sake of clarity,FIG. 4 shows a principal component load diagram example generated bycombining 25 period variables C36 to C60 and the indicator variable Q.

Step 7: The action directions and magnitudes of the period variables onthe indicator variable are classified for the principal component loaddiagram in step 6. FIG. 5 is a classification example, it can be seenfrom FIG. 5 that the actions of C154, C155, C156 and C273 on theindicator variable Q are maximum, wherein C154, C155 and C156 arereverse actions, and C273 is a positive action. C66, C111 and the likehaving an included angle of about 90 degrees with the indicator variableQ in the directions nearly do not act on the indicator variable Q.

Step 8: Mean value and standard deviation of each period variable arecalculated respectively. For example, the mean value of C154 having areverse action on the indicator variable Q is 134.58 DEG C, and thestandard deviation is 6.08 DEG C.

Step 9: The optimization target value of the ith period variable isacquired according to the following perturbation calculation formula:

J(i)=M(i)+sign(i)×3σ(i)

wherein J(i), M(i) and σ(i) herein are respectively optimization targetvalue, mean value and standard deviation of the ith period variable; andsign(i) is a cosine symbol of an included angle formed by the ith periodvariable and the indicator variable. On the classification diagram ofFIG. 5, the sign(i) is +1 when the included angle is smaller than 90degrees, −1 when the included angle is greater than 90 degrees, and 0when the included angle is equal to 90 degrees.

Step 10: The optimization target values of all periods obtained in step9 constitute a basic optimization variable curve according to a periodsequence i=1, 2, . . . , 300.

Step 11: Moving average filtering is performed on the basic optimizationcurve, so that the filtered optimization curve is relatively smooth andfacilitates later tracking control design. FIG. 6 is a comparison of theoptimized temperature curve and the original temperature curve, and FIG.9 shows an optimization curve after moving average filtering and anoriginal optimization curve. It can be seen from FIG. 9 that thefiltered optimization curve is smoother and facilitates implementationof a tracking controller.

Step 12: When the basic optimization control locus obtained by the aboveseries of steps is used on line, recursive error correction is performedin each time period:

(1) for the (i−1)th time period, the error of the offline basicoptimization target value J(i−1) and the actual measured value RV(i−1)is calculated:

E(i−1)=J(i−1)−RV(i=1)

(2) on the offline basic optimization strategy, a new optimizationtarget value of next period is constituted:

J _(o)(i)=J(i)+E(i−1).

Step 12 is sequentially calculated according to the period sequence i=1,2, . . . , 300, till the operation of the whole batch process is over.FIG. 7 is a generation calculation schematic diagram of an onlinerecursive error correction strategy.

FIG. 10 is an optimization result example of a batch crystallizationprocess. It can be seen from the result in the figure that theoptimization-free yield is 90.25%, whereas under the recursivecorrection optimization strategy, the actual operation yield is 94.88%and is substantially close to the theoretical optimal yield 95.51%. Thisresult shows the effectiveness and practicability of the method of thepresent invention.

While the present invention has been described in some detail forpurposes of clarity and understanding, one skilled in the art willappreciate that various changes in form and detail can be made withoutdeparting from the true scope of the invention. All figures, tables,appendices, patents, patent applications and publications, referred toabove, are hereby incorporated by reference.

What is claimed is:
 1. A model-free online recursive optimization methodfor a batch process based on variable period decomposition,characterized by comprising the following steps: (1) for operating acomplete batch process, acquiring variables to be optimized and finalquality or yield indicators in batches; (2) for the data acquired instep (1), performing principal component analysis on the variables inbatches, and removing singular points from a principal component modediagram, so as to enable all data points to be within one degree ofcredibility; (3) performing interval division on the remaining dataafter the singular points are removed on a time axis; expressing eachbatch of data included in each interval as a continuous variable,wherein these variables are referred to as decomposed period variables,and a value of the period variable is composed of each batch of data ofthe variable to be optimized in a specific time interval; (4) referringto each corresponding batch quality or yield indicator in step (3) as anindicator variable, wherein a value of the indicator variable is acontinuous variable formed by the quality or yield of each batch; (5)combining the period variables and the indicator variables formed instep (3) and step (4) to form a combined data matrix of the periodvariables and the indicator variables, and performing principalcomponent analysis on the combined data matrix to form a principalcomponent load diagram; (6) classifying the action directions andmagnitudes of the period variables on the indicator variables for theprincipal component load diagram in step (5); (7) calculating anoptimization strategy for each period variable according to thefollowing perturbation formula:J(i)=(i)+sign(i)×3σ(i) wherein J(i), M(i) and σ(i) herein arerespectively optimization target value, mean value and standarddeviation of the ith period variable; and sign(i) is a cosine symbol ofan included angle formed by the ith period variable and the indicatorvariable; (8) constituting a basic optimization variable curve for thewhole batch process by using the optimization target values of allperiods obtained in step (7) according to a period sequence; (9) in the(i−1)th time period, calculating an error of an offline basicoptimization target value J(i−1) and an actual measured value RV(i−1):E(i−1)=J(i−1)−RV(i−1); (10) on the offline basic optimization strategy,constituting a new optimization target value of next period:J _(o)(i)=J(i)+E(i−1); and (11) sequentially calculating step (9) andstep (10) according to the period sequence i=1, 2, . . . , N andapplying them to the process, till the operation of the whole batchprocess is over.
 2. The model-free online recursive optimization methodfor the batch process based on variable period decomposition accordingto claim 1, characterized in that the time intervals of batch processdata acquisition in step (1) are equal or unequal.
 3. The model-freeonline recursive optimization method for the batch process based onvariable period decomposition according to claim 1, characterized inthat the interval division in step (3) is equal interval division orunequal interval division.
 4. The model-free online recursiveoptimization method for the batch process based on variable perioddecomposition according to claim 1, characterized in that theclassification in step (6) comprises positive action, reverse action andno/micro action.
 5. The model-free online recursive optimization methodfor the batch process based on variable period decomposition accordingto claim 1, characterized in that the value of the included angle cosinesymbol sign(i) is +1 when the included angle is smaller than 90 degrees,−1 when the included angle is greater than 90 degrees, or 0 when theincluded angle is equal to 90 degrees.
 6. The model-free onlinerecursive optimization method for the batch process based on variableperiod decomposition according to claim 1, characterized in that theoptimization variable curve is digitally filtered in step (8), so thatthe new optimization variable curve is smooth.